Artificial Wrestling: A Dynamical Formulation of Autonomous Agents Fighting in a Coupled Inverted Pendula Framework

TL;DR

This paper introduces a coupled inverted pendula framework for autonomous agents that fight like humans, using intelligent controllers trained via off-line learning to produce dynamic, competitive interactions.

Contribution

It presents a novel coupled inverted pendula model with an intelligent control scheme that learns to produce desired final states, enabling autonomous fighting behavior.

Findings

Performance improves with added delay in the controller.

Quantization resolution affects control accuracy.

Autonomous fighting behavior demonstrated successfully.

Abstract

We develop autonomous agents fighting with each other, inspired by human wrestling. For this purpose, we propose a coupled inverted pendula (CIP) framework in which: 1) tips of two inverted pendulums are linked by a connection rod, 2) each pendulum is primarily stabilized by a PD-controller, 3) and is additionally equipped with an intelligent controller. Based on this framework, we dynamically formulate an intelligent controller designed to store dynamical correspondence from initial states to final states of the CIP model, to receive state vectors of the model, and to output impulsive control forces to produce desired final states of the model. Developing a quantized and reduced order design of this controller, we have a practical control procedure based on an off-line learning method. We then conduct numerical simulations to investigate individual performance of the intelligent controller, showing that the performance can be improved by adding a delay element into the intelligent controller. The result shows that the performance depends not only on quantization resolutions of learning data but also on delay time of the delay element. Finally, we install the intelligent controllers into both pendulums in the proposed framework to demonstrate autonomous competitive behavior between inverted pendulums.